In this paper, we consider a JS metric space endowed with convexity structure, which will allow us to examine and study convergence of Mann iteration and Ishikawa iteration for Banach type and Chatterjea type contractions defined on JS metric space.

<p>The genetic algorithm (GA) is an adaptive metaheuristic search method based on the process of evolution and natural selection theory. It is an efficient algorithm used for solving the combinatorial optimization problems, e.g., travel salesman problem (TSP), linear ordering problem (LOP), and job-shop scheduling problem (JSP). The simple GA applied takes a long time to reach the optimal solution, the configuration of the GA parameters is vital for a successful GA search and convergence to optimal solutions, it includes population size, crossover operator, and mutation operator rates. Also, very recently, many research papers involved the GA in coding theory, In particular, in the decoding linear block codes case, which has heavily contributed to reducing the complexity, and guaranting the convergence of searching in fewer iterations. In this paper, an efficient method based on the genetic algorithm is proposed, and it is used for computing the Automorphisms groups of low density parity check (LDPC) codes, the results of the aforementioned method show a significant efficiency in finding an important set of Automorphisms set of LDPC codes.</p>

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In this paper, we introduce four new types of contractions called in this order Kanan-S-type tricyclic contraction, Chattergea-S-type tricyclic contraction, Riech-S-type tricyclic contraction, Cirić-S-type tricyclic contraction, and we prove the existence and uniqueness for a fixed point for each situation.

Many research papers in coding theory have recently focused on designing high-rate codes or improving codes that exist through a better understanding and then improving the coding and decoding algorithm. As a result, this paper aims to investigate the computation of the Automorphisms groups of some optimal codes (e.g., some linear circulant codes where their distance meets the lower bound and nonlinear Nordstrom-Robinson (24, 28, 6) code). These Automorphisms groups provide information about the structure of the code, which aids in both the design and enhancement and improvement of decoding algorithms. A new genetic algorithm-based method is proposed, with a detailed description of its components, the fitness function, selection, crossover, and mutation, and is used to find an important collection of Automorphisms; the results obtained have shown that the proposed method is effective in finding stabilizers set for some types of codes.